Statistical process control approach to reduce the bullwhip effect

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007.Includes bibliographical references (leaves 66-68).The bullwhip effect is a pervasive problem in multi echelon supply chains that results in inefficient production operations and higher inventory levels. The causes of the bullwhip effect are well understood in industry and academia. Quantitative and qualitative solutions to attenuate this effect have been proposed in various research studies. In this research a quantitative solution in the form of a Statistical Process Control (SPC) based inventory management system is proposed that reduces the bullwhip effect while reducing inventory without compromising service level requirements for a variety of products. The strength of this methodology is in its effectiveness in reducing bullwhip for fast moving products in the mature phase of their lifecycles where improving production efficiency and lowering inventory investment are critical. However, fill rate issues are observed for slow moving products and therefore, the methodology is not recommended for such products. Finally, the application of this methodology to reduce the bullwhip effect is illustrated for a product family of a medical devices company. The results for the different classes of products in this family are discussed.by Harikumar Iyer [and] Saurabh Prasad.M.Eng.in Logistic

Is M&A Self-Dealing in the Context of Peer Benchmarking of CEO Pay?

We define two effects: (a) percentage difference between median CEO pay of compensation peers and their counterfactual peers (Peer pay effect, PPE), and (b) percentage difference between focal firm CEO pay and the median CEO pay of their compensation peers (CEO pay effect, CPE). We find a negative relation between M&A announcement period abnormal returns and pre-announcement PPE. The PPE (CPE) is lower (higher) in acquiring years relative to non-acquiring years. We show that the lower PPE is consistent with better governance and higher CPE is due to benchmarking against peers with higher median CEO pay and for completing acquisitions

Nonstandard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants